ENSO surface shortwave radiation forcing over the tropical Pacific

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ENSO surface shortwave radiation forcing over the

tropical Pacific

K. G. Pavlakis, N. Hatzianastassiou, C. Matsoukas, A. Fotiadi, I. Vardavas

To cite this version:

K. G. Pavlakis, N. Hatzianastassiou, C. Matsoukas, A. Fotiadi, I. Vardavas. ENSO surface shortwave radiation forcing over the tropical Pacific. Atmospheric Chemistry and Physics Discussions, European Geosciences Union, 2008, 8 (2), pp.6697-6728. �hal-00304077�

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ENSO surface shortwave radiation

over the tropical Pacific K. G. Pavlakis et al. Title Page Abstract Introduction Conclusions References Tables Figures ◭ ◮ ◭ ◮ Back Close

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Printer-friendly Version Interactive Discussion Atmos. Chem. Phys. Discuss., 8, 6697–6728, 2008

www.atmos-chem-phys-discuss.net/8/6697/2008/ © Author(s) 2008. This work is distributed under the Creative Commons Attribution 3.0 License.

Atmospheric Chemistry and Physics Discussions

ENSO surface shortwave radiation forcing

over the tropical Pacific

K. G. Pavlakis1,2,3, N. Hatzianastassiou1,4, C. Matsoukas1,5, A. Fotiadi1,3, and I. Vardavas1,3

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Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece 2

Department of General Applied Science, Technological Educational Institute of Crete, Greece 3

Department of Physics, University of Crete, Crete, Greece 4

Laboratory of Meteorology, Department of Physics, University of Ioannina, Greece 5

Department of Environment, University of the Aegean, Greece

Received: 12 February 2008 – Accepted: 4 March 2008 – Published: 7 April 2008 Correspondence to: K. Pavlakis (pavlakis@iesl.forth.gr)

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Printer-friendly Version Interactive Discussion Abstract

We have studied the spatial and temporal variation of the downward shortwave ra-diation (DSR) at the surface of the Earth during ENSO events for a 21-year period over the tropical and subtropical Pacific Ocean (40◦S–40◦N, 90◦E–75◦W). The fluxes were computed using a deterministic model for atmospheric radiation transfer, along

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with satellite data from the ISCCP-D2 database, reanalysis data from NCEP/NCAR for the key atmospheric and surface input parameters, and aerosol parameters from GADS (acronyms explained in main text). A clear anti-correlation was found between the downward shortwave radiation anomaly (DSR-A) time-series, in the region 7◦S– 5◦N 160◦E-160◦W located west of the Ni ˜no-3.4 region, and the Ni ˜no-3.4 index

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series. In this region where the highest in absolute value DSR anomalies are ob-served, the mean DSR anomaly values range from −45 Wm−2during El Ni ño episodes to +40 Wm−2 during La Ni ña events. Within the Ni ño-3.4 region no significant DSR anomalies are observed during the cold ENSO phase in contrast to the warm ENSO phase. A high correlation was also found over the western Pacific (10◦S–5◦N, 120–

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140◦E), where the mean DSR anomaly values range from +20 Wm−2 to −20 Wm−2 during El Ni ño and La Ni ña episodes, respectively. There is also convincing evidence that the time series of the mean downward shortwave radiation anomaly in the north subtropical Pacific region 7–15◦N 150–170◦E, precedes the Ni ño-3.4 index time-series by about 7 months. Thus, the downward shortwave radiation anomaly is a

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tary index to the SST anomaly for the study of ENSO events and can be used to assess whether or not El Ni ˜no or La Ni ˜na conditions prevail.

1 Introduction

The El Ni ˜no Southern Oscillation (ENSO) is the dominant natural interannual climate fluctuation on Earth and involves the interaction between the tropical Pacific Ocean

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Printer-friendly Version Interactive Discussion the tropical atmosphere creates changes in the global atmospheric circulation. Since

Bjerknes (1966, 1969) who first realized that the heart of the ENSO phenomenon is the ocean-atmosphere interaction, the dynamics of this pattern of climate variability, as well as variations in atmospheric and radiation variables associated with it, were studied by many workers (e.g., Philander, 1990; McCreary and Anderson, 1991; Neelin

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et al., 1998; Chou et al., 2004; Dijkstra, 2006; Wang and Fiedler, 2006; Pavlakis et al., 2007). Under normal conditions, the equatorial Pacific Ocean is characterized by warm surface waters in the west and cold surface waters in the east and by easterly trade winds in the lower atmosphere (Walker circulation). The trade winds as they flow over the Pacific Ocean pick up heat and moisture, bringing warm moist air over

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the Indonesian region (western Pacific) where it ascends and deep convection causes heavy precipitation. The warm phase of ENSO (El Ni ˜no) is associated with an unusual surface water warming of the eastern and central equatorial Pacific, a weakening of the easterly trade winds and a shift of strong convection from the western Pacific, where dry conditions prevail, into the central Pacific accompanied by heavier than usual rainfall

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there. La Ni ˜na, the ENSO cold phase, is the counterpart of El Ni ˜no, often following it and characterised by the opposite effects. Thus, ENSO is an oscillation between warm and cold events with a peak that typically occurs during the northern hemisphere winter (late December–early January). Over the past two decades a large number of studies have appeared, attempting to explain the mechanism of the oscillation between

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the two phases of the ENSO phenomenon and many models have been proposed (e.g. Suarez and Schopf, 1988; Cane et al., 1990; Jin, 1997a, 1997b; Picaut et al., 1997; Wang et al., 1999; Wang, 2001; Wang and Picaut, 2004). A more recent short review summarizing theories and mechanisms about El Ni ˜no variability is given by Dijkstra (2006).

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Despite recent progress, the ENSO onset time, duration, strength and spatial struc-ture have proved hard to simulate and forecast since it involves complex climatic pro-cesses and feedbacks between atmospheric and ocean properties (e.g. Fedorov et al., 2003, and references therein). Thus, measurements of different atmospheric and

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Printer-friendly Version Interactive Discussion ocean properties are essential to improve our understanding of ENSO evolution.

In-ternational monitoring programs of the coupled atmosphere-ocean system started in the Pacific around 1985 and led to the TAO/TRITON (Tropical Atmosphere Ocean project/Triangle Trans Ocean Buoy Network) array of moored buoys across the ocean. The aim of this program is to provide real-time ground-based measurements of winds,

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sea-surface temperature, sub-sea-surface temperature, sea level and ocean flow that help in the understanding of the physical processes responsible for ENSO (McPhaden et al., 1998). Satellite observations on the other hand, provide measurements of at-mospheric variables that include precipitation, cloud cover, humidity, and the radiation budget at the top of the atmosphere (Trenberth and Caron, 2000; Cess et al., 2001;

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Allan et al., 2002; Wagner et al., 2005; Wang and Fiedler, 2006). The variability and the spatial distribution of the values of ocean and atmospheric variables are not the same for all ENSO events. Thus, a definition of ENSO is necessary for the study of this phenomenon (Trenberth, 1997) using some of the ocean or atmospheric parameters for constructing an index to describe the phase and strength of ENSO events.

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eral different indices have been used in the literature, mostly based on SST. The SST based indices are obtained from the SST anomalies with respect to average values, over some specified region of the ocean (see for example, Trenberth and Stepaniak, 2001; Hanley et al., 2003). There is another index widely used, the Southern Oscil-lation index, which is related to air pressure differences at sea level, between Darwin

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(Australia) in the west and Tahiti in the east. There has been also an effort to combine several atmospheric-oceanic variables into a single index like the multivariate ENSO index (Wolter and Timlin, 1998). Averages of 850 mb wind, outgoing longwave radia-tion (OLR) at the top of the atmosphere as well as precipitaradia-tion over specific regions (Curtis and Adler, 2000) are also used, although not often, to monitor ENSO. Recently

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(Pavlakis et al., 2007) presented the downwelling longwave radiation (DLR) anomaly in the Ni ˜no-3.4 region as a useful index that contains information both for oceanic (i.e. SST) and atmospheric (i.e. water vapour, cloud amount) processes.

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Printer-friendly Version Interactive Discussion Although satellites monitor the Earth’s radiation budget at the top of the atmosphere,

they do not measure radiative fluxes at the Earth’s surface. However, the Earth’s cli-mate system is driven by the radiative energy balance between the solar shortwave radiation (SW) absorbed by the atmosphere and the surface of the Earth and the ther-mal longwave radiation (LW) emitted by the Earth to space. In this respect, ENSO

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events are expected to be associated with the spatial and temporal variability of the radiative energy balance at the surface over the tropical and subtropical Pacific ocean. Therefore, radiative transfer models remain as the only alternative for estimating the surface radiation budget variability and its influence on ENSO evolution. Specifically, the net heat flux into the ocean plays a key role in ENSO evolution and it is a significant

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variable in the models that have been developed to make ENSO predictions (Dijkstra, 2006). The variation of the net heat flux during ENSO events is of paramount impor-tance to the dynamics of the system (Harrison et al., 2002; Chou et al., 2004). The net heat flux into the ocean is a small residual of four terms; the net shortwave radia-tion at the surface (NSR), the latent heat loss, the sensible heat transfer and the net

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downwelling longwave radiation at the Earth’s surface (NSL). The NSL is the difference between the downward longwave radiation (DLR) at the Earth’s surface and the Earth’s surface thermal emission. In a previous paper (Pavlakis et al., 2007) we presented the NSL and DLR as well as their variability over the tropical and subtropical Pacific ocean during the warm and cold ENSO phases. There, the DLR-A [3.4] index based on DLR

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anomalies in the Ni ˜no 3.4 region was defined as a useful index to describe ENSO events and the DLR anomalies were used as a useful variable reflecting SST changes and water vapour variability in the lower atmosphere (see also Vardavas and Taylor, 2007). The NSR is the difference between the downwelling shortwave radiation (DSR) and the reflected radiation from the ocean surface. However, the reflected term is more

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than one order of magnitude smaller than the DSR, since the ocean albedo is less than 0.07. Thus, DSR dominates the net shortwave flux budget. The variability of DSR, the component of the net heat into the ocean with the largest magnitude, reflects mostly fluctuations in cloud cover caused by variations in atmospheric circulation and thus,

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Printer-friendly Version Interactive Discussion it is very important in order to describe and study the intensity or duration of ENSO

events.

Thus in this paper, we present DSR data generated by a deterministic radiation trans-fer model for the period 1984–2004 for the tropical and subtropical Pacific Ocean and examine their spatial and temporal variability during ENSO events. In addition, we

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vestigate the correlation of DSR anomalies with the Ni ˜no 3.4 index. In Sect. 2, we describe the radiation model and the input data used. In Sect. 3, the surface short-wave radiation distribution and its variation during warm and cold mature ENSO phases are presented. In Sect. 4, the DSR variation during ENSO evolution is examined. In Sect. 5, the correlation of the Ni ˜no 3.4 index and surface radiation parameters are

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sented. In Sect. 6, we present our conclusions. Table 1 lists the symbol definitions of the radiation parameters that are most often used in this paper.

2 Radiation model and data description

We use a shortwave (SW) radiative transfer model which is described in detail in Hatzianastassiou et al. (2004a) and Hatzianastassiou et al. (2005), where it was used

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to compute the top of the atmosphere (TOA) and surface SW radiation-budgets on a global scale. This model is based on a detailed radiative-convective model devel-oped for climate change studies (Vardavas and Carver, 1984; Vardavas and Taylor, 2007) and has been successfully tested (Hatzianastassiou and Vardavas, 1999, 2001) according to the Intercomparison of Radiation Codes in Climate Models (ICRCCM)

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program. The model results have been validated against Earth Radiation Budget Ex-periment (ERBE) S4 scanner data (Hatzianastassiou et al., 2004a) as well as against data from the Baseline Surface Radiative Network (BSRN) (Ohmura et al., 1998) and the Global Energy Balance Archive (GEBA) (Ohmura and Gilgen, 1993) in Hatzianas-tassiou et al. (2005). The same model has been also used on a regional scale to study

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trends in tropical mean SW radiation budget at TOA (Fotiadi et al., 2005) as well as to compute the spatial and temporal distribution of long-term shortwave surface radiation

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Printer-friendly Version Interactive Discussion over Greece (Fotiadi et al., 2006).

In brief, the model divides the SW radiation into two spectral bands, one essen-tially for the ultraviolet-visible wavelengths (λ<0.85 µm), accounting for about 60% of the total solar radiation, and a second band for the near-infrared wavelengths (0.85 µm≤ λ≤5 µm) accounting for about 40% of total solar radiation. The model takes

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into account the presence of ozone (O3), water vapor (H2O), carbon dioxide (CO2),

aerosol particles and Rayleigh scattering layers as well as low-, middle- and high-level cloud layers in the atmosphere, all above an isotropically reflecting Earth surface. The atmosphere is divided vertically in layers of about 5 hPa thickness and surface topog-raphy is considered. The surface reflectivity is computed for every pixel by considering

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four types of surface: land, ocean, snow and ice. A full presentation and discussion of the model can be found in Hatzianastassiou et al. (2005).

All of the cloud climatological data for our radiation transfer model were taken from the International Satellite Cloud Climatology Project (ISCCP-D2) data set (Rossow and Schiffer, 1999), the latest released ISCCP series of mean monthly cloud

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tologies, which provides monthly means for 72 climatological variables in 2.5-degree equal-angle grid-boxes for the period 1984–2004. The specific variables used in our model from ISCCP-D2 database are summarized in Fotiadi et al. (2006). The verti-cal profiles of the temperature and water vapour as well as the surface temperature were taken from the National Center for Environmental Prediction / National Centers

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for Atmospheric Research (NCEP/NCAR) reanalysis project (Kistler et al., 2001), cor-rected for topography as in Hatzianastassiou et al. (2001). These data are also on a 2.5-degree spatial resolution, monthly averaged and cover the same 21-year period as the ISCCP-D2 data. Data for aerosol optical depth, single scattering albedo and asym-metry parameter were taken from GADS (Koepke et al., 1997). A complete description

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of the aerosol data used is given in Hatzianastassiou et al. (2004b), and the treatment of aerosols in the SW model is described in Hatzianastassiou et al. (2005).

A full presentation and discussion of the DSR distribution over the globe can be found in Hatzianastassiou et al. (2005). There, a series of sensitivity tests were performed

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Printer-friendly Version Interactive Discussion to investigate how much uncertainty is introduced in the model DSR by uncertainties

in the input parameters, such as low, middle or high cloud amount, cloud absorption and scattering optical depth, cloud asymmetry factor as well as the total precipitable water. Also, comparisons between the monthly mean DSR fluxes used in this paper and those of higher spatial (1◦×1◦ latitude-longitude) or temporal (daily) resolution,

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both on a global and regional scale, have shown very good agreement, with correlation coefficients exceeding 0.98 (see Hatzianastassiou et al., 2005; Fotiadi et al., 2006).

3 Long-term surface shortwave radiation

The geographical distribution of the downward SW radiation (DSR) at the Earth’s sur-face for each month of the 21-year period 1984–2004 was computed at a spatial

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lution of 2.5◦(latitude)×2.5◦(longitude), over the tropical and subtropical Pacific Ocean (40◦S–40◦N, 90◦E–75◦W). During the 21-year study period (1984–2004) five signif-icant El Ni ˜no events occurred (1986–1987, 1991–1992, 1994–1995, 1997–1998 and 2002–2003) and the same number of significant La Ni ˜na events in 1984–1985, 1988– 1989, 1998–1999, 1999–2000 and 2000–2001 (Trenberth, 1997; Wang and Fiedler,

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2006).

We have calculated, for each 2.5×2.5 grid-box, the mean monthly DSR averaged over the 11 neutral years (DSRNE), i.e., the years when no significant El Ni ˜no or La

Ni ˜na events occurred, as a representative of normal conditions. The DSR_NEfor the 3-month period November, December and January (NDJ) is shown in Fig. 1a (top panel).

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The three month period NDJ is selected as best representing the mature phase of ENSO evolution, as the ENSO peak typically occurs during December–January when the strongest changes in the DSR occur.

As expected, the maxima in DSR over the ocean, reaching about 280 Wm−2, occur over the eastern Pacific (east of the date line), in the region near the equator (5◦S–5◦N)

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and in the southeastern subtropics (5◦S–30◦S 140◦W–100◦W), where subsidence re-gions are located with small total cloud amounts (Tian and Ramanathan, 2002). Over

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Printer-friendly Version Interactive Discussion the eastern Pacific, low values of DSR, about 170 Wm−2, are observed along the coasts

of Peru and west of the peninsula of Baja California where low-level clouds are predom-inant (Tian and Ramanathan, 2002; Amador et al., 2006) while even lower values of DSR of about 150 Wm−2 are found in the inter-tropical convergence zone (ITCZ) cen-tered around 8◦N, a region of strong upward motion of moist air.

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Over the west tropical Pacific (west of the date line), there is a single Hadley cell with the air rising over the western Pacific warm pool (10◦S–10◦N) where the highest open ocean water temperatures on Earth are observed. Thus high level clouds (cloud amount over 40%) are concentrated in these moist convective regions (Tian and Ra-manathan, 2002) and low DSR values of about 190–220 Wm−2 are observed there.

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Note that there is a difference of about 60–70 Wm−2 in DSR values between eastern and western Pacific around the equator.

We also computed, for each grid-box, the average DSR over the five years1 when El Ni ˜no (DSREN) conditions prevailed (Fig. 1b, middle panel) and the corresponding

average DSR over the five years when La Ni ˜na (DSR_LN) conditions prevailed (Fig. 1c,

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bottom panel), for the three month period of NDJ. It is evident from these figures that lower values of DSR are observed over the central and eastern Pacific, during the El Ni ˜no years due to the shift of atmospheric convection from the western Pacific warm pool to the central equatorial Pacific. Thus the east-west Pacific DSR gradient becomes smaller. On the other hand, high DSR values of up to 270 Wm−2are observed west of

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the date line during the La Ni ˜na years (Fig. 1c).

In Fig. 2 we show the distribution of the difference DSREN–DSRLN, over the tropical

and subtropical Pacific. This difference will be referred to as the El Ni ño DSR anomaly (DSR-A_LN) with respect to La Ni ña DSR. In the same figure, the rectangle designates the Ni ño-3.4 region (5◦S–5◦N, 170–120◦W) that is used to define the Ni ño-3.4 index,

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one of the indices based on sea surface temperature, for monitoring and identifying El Ni ño and La Ni ña events (Hanley et al., 2003). The Ni ño-3.4 region is used widely

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An “El Ni ˜no year” is defined, for our purposes, as starting in July and ending in June of the next year.

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Printer-friendly Version Interactive Discussion as a region with high SST anomalies and with a proximity to the main deep-convection

centres during ENSO events. As can be seen in Fig. 2 the DSR-ALNobtains the highest

values (in absolute terms), reaching a maximum of about −80 Wm−2, near the equator west of the Ni ño-3.4 region where the largest anomalies in sea surface temperatures (SST) are observed during El Ni ño events. Inside the Ni ño-3.4 region as well as in a

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region near the coast of Peru (0–5◦S, 120–90◦W) DSR-ALN is about −30 Wm −2

. In the western Pacific, on the other hand, the sign of the DSR-ALN is reversed, with the

DSR_ENbeing higher than DSR_LN by +30 Wm−2 east of Borneo, north of Australia and over the Philippines Sea.

4 DSR variation during ENSO evolution 10

Here we investigate the evolution of ENSO-related changes in the distribution and val-ues of DSR over the tropical and subtropical Pacific. In our subsequent analysis we use the three month period of August, September and October (ASO) to study the early stages of ENSO development, November, December and January (NDJ) for the ma-ture phase of El Ni ˜no or La Ni ˜na events, and February, March and April (FMA) for the

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decay phase of ENSO as in Pavlakis et al. (2007). In spite of the significant differences in the onset and evolution of individual ENSO events, the ASO, NDJ, and FMA periods provide a frame of reference for studying, in broad terms, the evolution of ENSO. 4.1 El Ni ˜no events

We now define the difference DSREN–DSRNEas the El Ni ˜no DSR anomaly with respect 20

to the average normal condition (El Ni ˜no DSR-A_NE). In Fig. 3 (left-hand side) we show the distribution of El Ni ˜no DSR-ANE for the three-month periods of ASO (top panel),

NDJ (middle panel) and FMA (bottom panel), at 2.5×2.5 spatial resolution. In order to investigate and identify the regions that show significant changes in the DSR during El Ni ˜no years with respect to values under normal conditions, we performed for each

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Printer-friendly Version Interactive Discussion grid-box and for each three-month period (ASO, NDJ, FMA) a two-tailed Student’s

t-test. Our two samples are the three-monthly DSR values of the 11 neutral years within our study period 1984–2004 and the corresponding values for the 5 years when El Ni ˜no conditions prevailed. The null hypothesis is that the mean values of the two samples are equal and the alternative hypothesis is that these values are different. On

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the right-hand side of Fig. 3 we show the geographical distribution of the P-values for the ASO (top panel), NDJ (middle panel) and FMA (bottom panel). Grid-boxes with P-values smaller than 0.05 are considered to have statistically significant El Ni ˜no DSR-ANEvalues.

The lowest values of DSR-A_NEduring the early stage of El Ni ˜no development (around

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−35 Wm−2) are found in the equatorial Pacific west of the Ni ˜no-3.4 region, the area where high positive SST anomalies (ocean warming) are observed (Wang, 2002). In contrast, high DSR-ANE values around +25 Wm

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are observed in the equatorial western Pacific over Indonesia east of Borneo. It is the same region (5◦S–5◦N, 120– 140◦E), termed Ni ˜no-5 by Wang and Weisberg (2000), where westerly wind anomalies

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were found to peak about six to eight months before those in the Ni ˜no-4 region. Values of DSR-A_NEaround −20 Wm−2are observed over the region 150–180◦E centred near 10◦N, which is consistent with the intensification of ascending air motion in roughly the same region as shown by Wang (2002) during the ENSO transition phase (the three month period of July, August, and September prior to the El Ni ˜no peak). For all the

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regions referred to above the statistical significance of the anomalies is very high with P-values lower than 0.01.

During the mature phase of El Ni ˜no, negative values of DSR-A_NE are observed in a region confined around the equator, between 10◦S–5◦N that extends from 160◦E to the coast of Peru. The lowest values of the DSR-ANEreach about −60 Wm

−2

, and

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are observed over the region where the deep atmospheric convection shifts from the western Pacific warm pool, west of the warmest SST anomalies in the Ni ˜no-3.4 and Ni ˜no-4 regions. The P-values there are the lowest observed over the whole Pacific (less than 0.001). There opaque clouds associated with deep convection, are bright

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Printer-friendly Version Interactive Discussion reflectors of solar radiation and thus act to reduce the surface solar radiation. In the

same region, negative outgoing longwave radiation (OLR) anomalies are observed at the top of the atmosphere (Wang, 2000; Wang and Fiedler, 2006) because due to deep convection, cloud tops get higher, and the radiation to space from clouds is at a lower temperature. No significant downward longwave radiation anomalies (DLR-As)

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are observed at the surface of the ocean (Pavlakis et al., 2007) and thus, in this region, DSR is the radiative component that mostly contributes to the fluctuation of the net heat flux into the ocean during El Ni ˜no. The maximum DLR-As about 20 Wm−2, are observed within the Ni ˜no-3.4 region causing radiative warming of the ocean surface there (Pavlakis et al., 2007) and coincide with the high SST anomalies. The SST

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anomalies are linked to the water vapour content of the atmosphere whose fluctuation in the lower atmosphere is the major factor that causes the DLR-As. In the Ni ˜no-3.4 region the mean value of DSR-ANEis about −30 Wm

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, and thus the radiative cooling of the ocean surface, during the warm ENSO phase, is moderated with respect to the neighbouring west of Ni ˜no-3.4 tropical region. In the west Pacific DSR-ANEvalues are 15

of opposite sign (positive), they are smaller in absolute magnitude than those in the eastern/central Pacific and are observed off the equator, north of Australia, north of New Guinea and over the Philippines and part of the South China Sea, with P-values less than 0.03.

During the decay phase of El Ni ˜no, DSR variability is centred along the equator in

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the central Pacific. Values of DSR-ANE around −30 Wm −2

are observed roughly in the same equatorial central Pacific region as during the mature ENSO phase, but in the eastern Pacific this region now shifts north of the equator up to the south coast of Mexico (20◦N). The out-of-phase behaviour in DSR-ANE values between the eastern

and the western Pacific is also observed during the decay phase of El Ni ˜no. In the

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western Pacific DSR-A_NE values of up to +20 Wm−2 with high statistical significance (P-values less than 0.02) are observed over the South China Sea, while the feature observed north of Australia during the mature ENSO phase has now migrated south-eastward.

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Printer-friendly Version Interactive Discussion 4.2 La Ni ˜na events

A similar analysis was conducted for the DSR anomalies during La Ni ña events. The various parameters are defined in the same way as in Sect. 4.1, but for the La Ni ña years. Instead of the suffix EN (El Ni ño), we use here the suffix LN (La Ni ña). The resulting geographical distribution of the La Ni ña DSR anomalies (DSR-A_NE), with

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spect to normal conditions, is shown on the left in Fig. 4 in the top panel for ASO, the middle panel for NDJ and the bottom panel for FMA. Large positive values (i.e. higher DSR for the La Ni ˜na years) of about +40 Wm−2are observed west of the dateline, in the region 7◦S–5◦N, 160–180◦E during the mature ENSO phase (i.e. during NDJ) while the corresponding values during the early phase of La Ni ˜na are about 30 Wm−2 over

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the same region. On the right side of Fig. 4 we present the geographical distribution of P-values, which confirms that the region west of the dateline, indicated above, displays statistically significant anomalies during all three phases (i.e. ASO, NDJ, FMA) of La Ni ˜na with P-values less than 0.02.

Note that there are no significant values of the DSR-ANEin the Ni ˜no-3.4 region during 15

the mature La Ni ˜na phase in contrast to the El Ni ˜no phase. In contrast, significant DLR-A with a mean value of about −15 Wm−2is observed in the same region during ENSO cold phase (Pavlakis et al., 2007) and thus, DLR is the radiative component that mainly contributes there to the fluctuation of the net heat flux into the ocean during the ENSO cold phase. DSR-A_NEvalues near −25 Wm−2are observed around the equator in the

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western Pacific east of Borneo and over Indonesia during the mature phase but the feature splits into two sub-features north and south of the original feature during the La Ni ˜na decay phase.

5 Correlation of Ni ˜no-3.4 index and DSR anomaly

The Ni ˜no-3.4 index based on sea surface temperature (SST) is used extensively in

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Printer-friendly Version Interactive Discussion is quantified as the three-month smoothed SST departures from normal SST, in the

Ni ˜no-3.4 region of the equatorial Pacific. In Fig. 5 we show the geographical distri-bution of the correlation coefficient given by linear regression of the time-series of the anomaly of the mean monthly DSR at the surface with respect to the average monthly DSR for the whole study period 1984–2004 (DSR-A) in each 2.5×2.5◦grid-box and the

5

Ni ño-3.4 index time-series. Although there are low values of the correlation coefficient (anti-correlation) over the entire Ni ño-3.4 region, the minimum values of the correlation coefficient (strongest anti-correlation) are observed west of the Ni ño-3.4 region in the zone 7◦S–5◦N 160◦E–160◦W. This region will be referred to as the central Pacific re-gion. The centre of deep convection is located there and the clouds that are formed

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block the downwelling solar radiation and at the same time (i.e. during the peak of the warm ENSO phase) the highest SSTs anomalies are observed in the Ni ˜no-3.4 region. In the western Pacific there is a positive correlation between the DSR-A and Ni ˜no-3.4 index time-series although in absolute terms the correlation coefficients are not as high as in the central Pacific. The maximum values of the correlation coefficient in the

west-15

ern Pacific are observed in the region 10◦S–5◦N 120–140◦E, which will be referred to as the western Pacific region. Finally, another very interesting region is the 7–15◦N 150–170◦E region (we shall refer to this as the north subtropical region), where al-though the correlation coefficients are very low there is a time shift between the DSR-A time series and the Ni ˜no-3.4 index time-series (we analyze these time-series later in

20

this section).

We have calculated the 3-month smoothed anomaly of the mean monthly DSR at the surface with respect to the average monthly DSR for the whole study period 1984–2004 (DSR-A), for each of the Ni ˜no-3.4, central Pacific, western Pacific and north subtropical region, respectively.

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Figure 6a shows the time-series of the DSR-A for the period 1984–2004 (black line) for the Ni ˜no-3.4 region. For comparison we have overlaid on the same diagram the time-series of the Ni ˜no-3.4 SST index (red line). There is a clear anti-correlation be-tween the two time-series. It is also evident that the maximum DSR change during

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Printer-friendly Version Interactive Discussion warm (El Ni ˜no) or cold (La Ni ˜na) ENSO events occurs between November and

Jan-uary. The DSR-A during the 1991–1992 and 1997–1998 El Ni ˜nos take the highest absolute values (about 55 Wm−2) because during these events the centre of convec-tive activity has moved into the Ni ˜no-3.4 region. Note however that the absolute values of DSR-A during the warm ENSO events are much higher than the corresponding

val-5

ues during cold ENSO events. This is due to the fact that the region of significant DSR anomalies during the cold ENSO phase lies to the west of the Ni ño-3.4 region (see Fig. 4a and b and discussion there). Thus, by means of DSR-A, no signal is observed in the Ni ño-3.4 region during the La Ni ña phase as clearly shown in Fig. 7a where a scatter plot between DSR-A in Ni ño-3.4 region and the Ni ño-3.4 index is presented.

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Figure 6b shows the time-series of the DSR-A (black line) in the central Pacific region (7◦S–5◦N 160◦E–160◦W) and on the same diagram we have overlaid the time-series of the Ni ño-3.4 SST index (red line). The DSR-A is out-of-phase with the Ni ño-3.4 index. There is an excellent anti-correlation in two neighbouring regions between the Ni ño-3.4 index (a sea parameter) over the Ni ño-3.4 region and the DSR-A over the

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central Pacific region that reflects mostly the variations in cloud amount caused by at-mospheric circulation anomalies. We have calculated the 3-month smoothed anomaly of the mean monthly total cloud amount with respect to the average monthly total cloud amount for the study period 1984–2004 for the central Pacific region. A linear re-gression between the DSR-A time series and the total cloud amount anomaly time

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series yielded a correlation coefficient of r=−0.91 (anti-correlation) showing that cloud amount variations are the primary determinants of the DSR variability. Thus in the central Pacific region the high DSR-A is correlated but not collocated with the region of high anomalies in SST (Ni ˜no-3.4 region). The DSR-A in the central Pacific has val-ues as low as −45 Wm−2(during the 1986–1987 El Ni ˜no), and as high as +40 Wm−2

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(during the 1999–2000 La Ni ˜na). Note that the 1991–1992 and 1997–1998 ENSO warm events are not the strongest ones with respect to the DSR anomaly. However, for all ENSO events (both warm and cold) high DSR-A values are observed, in ab-solute terms. A linear regression between DSR-A time-series in the central Pacific

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Printer-friendly Version Interactive Discussion region and the Ni ˜no-3.4 index yielded a correlation coefficient of r=−0.83 and a slope

of −15.4±0.6 Wm−2/◦C, as shown in Fig. 7b.

In the western Pacific region (10◦S–5◦N 120–140◦E) there is a positive correlation between the time-series of DSR-A and the Ni ˜no-3.4 index, as shown in Fig. 8a. In both time-series there are clear peaks during El Ni ˜no events and minima for the La

5

Ni ña events. The relative strengths of warm and cold ENSO events are very similar with DSR-A values reaching as high as +20 Wm−2during the 1991–1992, 1997–1998 and 2002–2003 El Ni ño events and as low as −20 Wm−2during the 1988–1989, 1995– 1996 and 1998–1999 La Ni ña events. Linear regression between DSR-A time-series in the western Pacific region and the Ni ño-3.4 index yielded a correlation coefficient of

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r_{=0.78 and a slope of 8.8±0.4 Wm}−2/◦C, as shown in Fig. 8b.

In the north subtropical Pacific region the 3-monthly smoothed DSR anomalies seem to precede the appearance of significant SST anomalies in the Ni 3.4 region (Ni ño-3.4 index) as shown in Fig. 9a. It is clear that the DSR-A shows a minimum before the peak of the Ni ño-3.4 index for each El Ni ño event. The minimum value of the DSR-A

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is about −20 Wm−2before the 1991–1992, 1997–98, and the 2002–2003 El Ni ˜no. We have, subsequently, calculated the correlation coefficient between the Ni ˜no-3.4 index time-series and the average 3-monthly smoothed DSR-A time-series in the north sub-tropical Pacific region shown in Fig. 9a, shifted by up to 12 months. In Fig. 9c we have plotted the value of this correlation coefficient (the values are negative, because the

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DSR anomalies and the Ni ˜no-3.4 index are anti-correlated) as a function of the time lag introduced in months. It is obvious that the highest anti-correlation is observed when the DSR-A time-series is shifted by 7 months. The value of the correlation coeffi-cient decreases from −0.09 with no time shift (no correlation), to −0.61 with a 7-month time shift. This means that DSR anomalies in the north subtropical Pacific region

pre-25

cede the anomalies in the Ni ño-3.4 region by 7 months. In Fig. 9b where the DSR-A time-series has been shifted by 7 months are shown clear minima of DSR-A for each peak of the Ni ño-3.4 index. Wang (2002) also found an anomalous ascending motion near 10N and 120-170E during the El Ni ño transition phase, i.e. 5–6 months prior to

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Printer-friendly Version Interactive Discussion each El Ni ˜no peak (note the overlap of our north subtropical Pacific region with that of

Wang (2002)).

6 Conclusions

Our model calculations, which are based on ISCCP-D2 cloud climatologies, temper-ature and humidity profile information from NCEP/NCAR reanalysis and aerosol data

5

from GADS, show a high variability in the downward shortwave radiation (DSR) at the surface of the Earth over the tropical and subtropical Pacific Ocean during ENSO events. We have found that the DSR decreases during warm ENSO phases, compared with neutral years, in a broad area in the central and eastern Pacific. This decrease in DSR (DSR-ANE), for the three-month period November, December and January, cor-10

responding to the mature phase of El Ni ˜no is larger than 15 Wm−2, in a broad area around the equator in the central Pacific extending to the coast of South America. A minimum DSR-ANE, of about −60 Wm

−2

, is observed west of the Ni ˜no-3.4 region. The DSR variability is associated mainly with variations in cloud amount. An increase in the amount of opaque and hence more reflecting clouds associated with deep convection

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results in less solar radiation reaching the surface.

During the cold phases of ENSO in the central Pacific, values of DSR-ANE greater

than +30 Wm−2 are observed west of the dateline, (7◦S–5◦N, 160◦E–180◦E) and in a more confined region compared with the corresponding one where negative DSR-ANEvalues prevail during the ENSO warm phases. Moreover, the maximum DSR-ANE 20

values are observed west of the corresponding minimum DSR-ANE values during the

warm phases of ENSO. Within the Ni ˜no-3.4 region no significant DSR anomalies are observed during the cold ENSO phase.

The Ni ˜no-3.4 index is very often used to define the phase and strength of ENSO events. We calculated the correlation coefficient given by linear regression of the

time-25

series of the monthly DSR anomaly, for the study period 1984–2004, in each 2.5×2.5◦ grid-box and the Ni ˜no-3.4 index, and found values less than −0.5 (anti-correlation) over

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Printer-friendly Version Interactive Discussion the central tropical Pacific. Correlation coefficient values of about −0.8 are observed

west of the Ni ño-3.4 region and around the equator (7◦S–5◦N, 160◦E–160◦W). Thus, in this region, the average DSR anomaly, is a very useful index to describe and study ENSO events and can be used to assess whether or not El Ni ño or La Ni ña conditions prevail.

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The DSR in the western Pacific shows less variability between warm and cold ENSO events compared with the same parameter in the central and eastern tropical Pacific. We investigated the covariability between the time-series of the mean regional DSR anomaly in the western Pacific (10◦S–5◦N, 120–140◦E) and the Ni ˜no-3.4 index, and found a significant correlation (coefficient equal to 0.6). The DSR-A changes between

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+20 Wm−2and −20 Wm−2between warm and cold ENSO events, respectively.

We further investigated the covariability between the time-series of mean regional anomalies of DSR (DSR-A) in the north subtropical region (7-15N 150-170E) and the Ni ˜no-3.4 index. The DSR-A in this region leads the corresponding Ni ˜no-3.4 index by about 7 months. Thus DSR measurements in this region will be very useful for the

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study of the time evolution of El Ni ˜no events.

Acknowledgements. The ISCCP-D2 data were obtained from the NASA Langley Research

Center (LaRC) Atmospheric Sciences Data Center (ASDC). The NCEP/NCAR Global Reanal-ysis Project data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Cooperative Institute for Research in Environmental Sciences (CIRES) Climate Diag-20

nostics Center, Boulder, Colorado, USA. The GADS data were obtained from the Meteorologi-cal Institute of the University of Munich, Germany (http://www.meteo.physik.uni-muenchen.de/

strahlung/aerosol/aerosol.htm).

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Table 1. Definition of symbols used to represent radiation parameters.

Symbol Definition

DSR Downward shortwave radiation at surface

DSR_EN, DSR_LN, DSR_NE Mean DSR when El Ni ˜no, La Ni ˜na, or neutral conditions prevail, respectively

DSR-A_LN El Ni ˜no DSR anomaly with respect to the mean DSR for the

La Ni ˜na years equal to DSR_EN-DSR_LN

DSR-A_NE El Ni ˜no or La Ni ˜na DSR anomaly with respect to the mean DSR

for the neutral years equal to DSR_EN-DSR_NEor DSR_LN-DSR_NE

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(a)

(b)

(c)

Fig. 1. The distribution of downward shortwave radiation at the surface (DSR), over the tropical

and subtropical Pacific for the three month period November, December, January (NDJ); (a) eleven neutral years average, (b) average for five El Ni ˜no years, (c) average for five La Ni ˜na years.

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Fig. 2. Differences in the mean downward shortwave radiation at the surface (DSR), between

the El Ni ˜no and La Ni ˜na years, over the tropical and subtropical Pacific for the period of Novem-ber, December and January (NDJ).

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(a) (b)

(c) (d)

(e) (f)

Fig. 3. Left: the distribution at 2.5×2.5 spatial resolution of El Ni ˜no DSR-A_NE for ASO (top panel), NDJ (middle panel), and FMA (bottom panel). Right: the distribution of P-values from a Student’s t-test.

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(a) (b)

(c) (d)

(e) (f)

Fig. 4. Left: the distribution at 2.5×2.5 spatial resolution of La Ni ˜na DSR-A_NE for ASO (top panel), NDJ (middle panel), and FMA (bottom panel). Right: the distribution of P-values from a Student’s t-test.

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Fig. 5. Geographical distribution of correlation coefficient between DSR-A and the Ni ˜no-3.4

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Printer-friendly Version Interactive Discussion (a) 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 -60 -40 -20 0 20 40 60 -4 -2 0 2 4 D S R A (W m -2 ) Nino-3.4 region 5S-5N 170-120W La Nina La Nina La Nina La Nina Year El Nino El Nino El Nino El Nino El Nino N in o 3 .4 i n d e x ( o C ) (b) 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 -60 -40 -20 0 20 40 60 -4 -2 0 2 4 D S R A (W m -2 ) 7S-5N 160E-160W La Nina La Nina La Nina La Nina Year El Nino El Nino El Nino El Nino El Nino N in o 3 .4 i n d e x ( o C )

Fig. 6. Time-series of downward shortwave radiation anomaly (DSR-A) at the surface (defined

with respect to the average monthly DSR for the whole study period 1984–2004) in the Ni ˜no-3.4 region (a), and in the central Pacific region (7◦

S–5◦ N 160◦

E–160◦

W) (b). Overlaid is the time-series of the Ni ˜no-3.4 index (red line).

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Printer-friendly Version Interactive Discussion (a) -3 -2 -1 0 1 2 3 -80 -60 -40 -20 0 20 Nino3.4 anomalies D S R A ( W m -2 ) Nino-3.4 region 5S-5N 170-120W (b) -3 -2 -1 0 1 2 3 -60 -40 -20 0 20 40 60 r : -0.83 slope: -15.4±0.6 Wm-2/ oC 7S-5N 160E-160W Nino3.4 anomalies D S R -A ( W m -2 )

Fig. 7. Scatter plot between the Ni ˜no-3.4 index and the DSR-A in the: (a) Ni ˜no-3.4 region and (b) the central Pacific region (7◦S–5◦N 160◦E–160◦W).

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Printer-friendly Version Interactive Discussion (a) 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 -40 -20 0 20 40 -4 -2 0 2 4 D S R A (W m -2 ) 10S-5N 120E-140E La Nina La Nina La Nina La Nina Year El Nino El Nino El Nino El Nino El Nino N in o 3 .4 i n d e x ( o C ) (b) -3 -2 -1 0 1 2 3 -40 -20 0 20 40 r : 0.78 slope: 8.8±0.4 Wm-2/ oC 10S-5N 120E-140E Nino3.4 anomalies D S R -A ( W m -2 )

Fig. 8. (a) Time series of downward shortwave radiation anomaly (DSR-A) at the surface in

the western Pacific region 10◦ S–5◦

N, 120–140◦

E (black line) compared with the time series of the Ni ˜no-3.4 index (red line), (b) Scatter plot between the DSR-A and the Ni ˜no-3.4 index in the western Pacific region.

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Printer-friendly Version Interactive Discussion (a) 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 -40 -20 0 20 40 -4 -2 0 2 4 D S R A (W m -2 ) 7N-15N 150E-170E La Nina La Nina La Nina La Nina Year El Nino El Nino El Nino El Nino El Nino N in o 3 .4 i n d e x ( oC ) (b) 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 -40 -20 0 20 40 -4 -2 0 2 4 D S R A (W m -2 ) 7N-15N 150E-170E La Nina La Nina La Nina La Nina Year El Nino El Nino El Nino El Nino El Nino N in o 3 .4 i n d e x ( o C ) (c) 0 2 4 6 8 10 12 -0,7 -0,6 -0,5 -0,4 -0,3 -0,2 -0,1 0,0 C o rr e la ti o n o f N in o -3 .4 i n d e x a n d D S R -A ti m e -s e ri e s i n n o rt h s u b tr o p ic a l re g io n month shift

Fig. 9. (a) Time-series of downward shortwave radiation anomaly (DSR-A) at the surface

(defined with respect to the average monthly DSR for the whole study period 1984–2004) in the north subtropical region (7–15◦

N 150–170◦

E). Overlaid is the time-series of the Ni ˜no-3.4 index (red line). (b) The same as (a) but with the DSR-A time-series shifted by 7 months. (c) correlation coefficient of the Ni ˜no-3.4 index and the DSR-A in the 7◦N–15◦N 150–170◦E region as a function of the number of months shift of the DSR-A.